(April 10, 2008 10:30 AM - 11:30 AM)

Abstract

The biotechnological advances in the last decade have enabled the possibility of a reverse problem formulation for the modeling of systems structure and dynamics of genetic and metabolic networks. Some major challenges for the development of these reverse engineering methods are related to the construction of efficient algorithms to build robust models with respect to data noise and feasible ways to combine gene expression data with a priori knowledge to produce functional predictions of such networks.

In this talk, we will introduce an evolutionary computation based reverse engineering algorithm for constructing the underlying network structure and dynamics from gene expression data and combine it, when available, with a priori knowledge; in our proposed method, gene expression data include wildtype time courses as well as knockout perturbations. Our framework is that of polynomial dynamical systems (PDS) enabling the use of computational algebra tools to efficiently describe structural characteristics of the desired models. Experiments on artificial genetic networks such as the segment polarity gene network in D. Melanogaster, show the performance of the proposed algorithm in constructing a robust (with respect to data noise) mathematical model.

The MBI receives major funding from the National Science Foundation Division of Mathematical Sciences and is supported by The Ohio State University.
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